Keywords:
Artificial Intelligence, Lung, Oncology, CT, CAD, CT-Quantitative, Computer Applications-Detection, diagnosis, Segmentation, Cancer
Authors:
J. Murchison, G. Ritchie, D. Senyszak, E. J. R. Van Beek; Edinburgh/UK
DOI:
10.26044/ecr2019/C-3685
Results
A total of 337 CT scans from 314 subjects (173 women,
164 men) with a total of 470 pulmonary nodules (largest axial diameter between ≥3mm and ≤30mm) were included in this study.
The mean age of all the subjects was 63 ± 7 years (range 32-88 years).
Details regarding the number of CT scans and nodules per group are described in table 1.
The mean largest axial diameter of all nodules in groups 1 to 5 was 7.68 ± 3.50 mm (range: 3.42 - 28.45 mm) and the mean volume was 198 ± 333 mm3 (range: 21 - 2797 mm3).
The total number of nodules in group 3 and 4 was 68 and 42,
respectively.
The total number of nodule-pairs in groups 3 and 4 was 23.
The sensitivity for detecting nodule pairs of CAD alone was 100.0% and the average number of FP-pairs was 0.0.
The mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02,
2.21) and 1.35 (95% CI: 1.01,
4.99),
respectively.